High-throughput sequencing technology is central to our current understanding of the human methylome. The vast majority of studies use chemical conversion to analyse bulk-level patterns of DNA methylation across the genome from a population of cells. While this technology has been used to probe single-molecule methylation patterns, such analyses are limited to short reads of a few hundred basepairs. DNA methylation can also be directly detected using Nanopore sequencing which can generate reads measuring megabases in length. However, thus far these analyses have largely focused on bulk-level assessment of DNA methylation. Here, we analyse DNA methylation in single Nanopore reads from human lymphoblastoid cells, to show that bulk-level metrics underestimate large-scale heterogeneity in the methylome. We use the correlation in methylation state between neighbouring sites to quantify single-molecule heterogeneity and find that heterogeneity varies significantly across the human genome, with some regions having heterogeneous methylation patterns at the single-molecule level and others possessing more homogeneous methylation patterns. By comparing the genomic distribution of the correlation to epigenomic annotations, we find that the greatest heterogeneity in single-molecule patterns is observed within heterochromatic partially methylated domains (PMDs). In contrast, reads originating from euchromatic regions and gene bodies have more ordered DNA methylation patterns. By analysing the patterns of single molecules in more detail, we show the existence of a nucleosome-scale periodicity in DNA methylation that accounts for some of the heterogeneity we uncover in long single-molecule DNA methylation patterns. We find that this periodic structure is partially masked in bulk data and correlates with DNA accessibility as measured by nanoNOMe-seq, suggesting that it could be generated by nucleosomes. Our findings demonstrate the power of single-molecule analysis of long-read data to understand the structure of the human methylome.

中文翻译：

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对长读长 DNA 甲基化的全基因组单分子分析揭示了反映核小体组织的异染色质异质模式。

高通量测序技术是我们目前了解人类甲基化组的核心。绝大多数研究使用化学转化来分析细胞群基因组中 DNA 甲基化的批量水平模式。虽然该技术已用于探测单分子甲基化模式，但此类分析仅限于数百个碱基对的短读取。DNA 甲基化也可以使用 Nanopore 测序直接检测，该测序可以生成测量兆碱基长度的读数。然而，到目前为止，这些分析主要集中在 DNA 甲基化的批量水平评估上。在这里，我们分析了人类淋巴母细胞的单个 Nanopore 读数中的 DNA 甲基化，以表明批量水平指标低估了甲基化组中的大规模异质性。我们利用相邻位点之间甲基化状态的相关性来量化单分子异质性，发现异质性在整个人类基因组中存在显着差异，一些区域在单分子水平上具有异质甲基化模式，而其他区域则具有更均质的甲基化模式。通过比较与表观基因组注释相关性的基因组分布，我们发现单分子模式的最大异质性是在异染色质部分甲基化结构域（PMD）内观察到的。相比之下，源自常染色质区域和基因体的读数具有更有序的 DNA 甲基化模式。通过更详细地分析单分子模式，我们证明了 DNA 甲基化中存在核小体尺度的周期性，这解释了我们在长单分子 DNA 甲基化模式中发现的一些异质性。我们发现这种周期性结构在大量数据中被部分掩盖，并且与 nanoNOMe-seq 测量的 DNA 可及性相关，表明它可能是由核小体产生的。我们的研究结果证明了对长读数据进行单分子分析以了解人类甲基化组结构的能力。